A picture is worth a thousand words is as true today as it ever was. Visual images form a fundamental part of our lives, from body language to subliminal messages buried in a picture. Therein lies the challenge; namely, to have a computer "read" visual images and interpret the true message of the image.
The usefulness of such intelligent visual imaging computers is far reaching. On a simple level, the imaging computer could be built into a copy machine (or a facsimile machine) and used to clean the copied image. This could be accomplished, for example, by having the computer scan the image, determine the proper outlines or other pertinent characteristics of the image, and then enhance those characteristics while removing any imperfections, such as extraneous dots and dark marks.
Other practical applications of such an imaging computer could be to monitor the physical movements of a person's extremities, such as a hand or arm, and determine from the movements what the person is saying. This type of action would also require image processing of a high degree.
Still other applications of such a processing system would be the understanding of symbols and relationships so that the symbols in a document, whether taken singularly or as a whole, would be "understood" by the imaging processor and used for creating the appropriate response as an output.
To accomplish such a formidable task, the image processing computer would have to process a vast amount of image pixel data in real time and manipulate those pixels in conjunction with many data bases. When it is understood that each image line of a typical visual image screen contains 1024 pixels, (high definition imaging systems have over 2000 pixels) with each pixel containing, perhaps as many as thirty-two data bits, with a typical image containing perhaps 1,000 lines, the magnitude of this task can be appreciated. Coupled with this, some of the pixels must be compared with other pixels, some pixels must be manipulated in certain ways, while still other pixels must be transformed according to special algorithms. All of this results in a magnitude of operations heretofore only accomplished by the largest, fastest computers.
In addition, because of the different types of operations such an imaging processor would be called upon to perform, very different internal structures must be utilized. In one situation it is desirable to have many processors, each capable of working independently on different parts of an image. This independence then presumes a parallel processing structure with the different processors having concurrent access to any memory area. However, in situations where all of the image must be processed dependent upon pixel information of many other parts of the image, an operating structure is required where many processors, or a single processor, has access to one or more memories, perhaps on an exclusive basis. Making matters even more difficult is the fact that during the actual processing of an image, several different competing internal operating structures may be necessary, thereby requiring close internal system communication.
It is thus a desirable objective to create an image processing system capable of fast, efficient image pixel manipulations while doing so at a cost and at a size where the processing can be incorporated easily into daily life.
Thus, it is desirable to have a relatively compact processing system having large memory capacity as well as large processing capacity while still maintaining the flexibility of accomplishing a myriad of diverse and structurally competitive processing feats.